A novel approach to testing for bioequivalence based on modeling the within-period dependence structure

Bioequivalence trials are commonly conducted to assess therapeutic equivalence between a generic and an innovator brand drug. In such trials, drug concentrations are obtained repeatedly over time and a metric such as the area under the concentration versus time curve is computed for each subject. Standard methods are then applied to conduct tests of average bioequivalence. A major disadvantage of this approach is the loss of information encountered when ignoring the correlation structure between repeated measurements. We propose a general linear model approach, incorporating the within-subject covariance structure, for making inferences. We investigate and compare the inferential properties of our proposed method with the traditional two one-sided tests approach using Monte Carlo simulation studies. We also examine the properties of the method in the event of missing data. Simulations show that the proposed approach is a more cost-effective viable alternative to the traditional method with superior inferential properties. Inferential advantages are particularly apparent in the presence of missing data. To illustrate our approach, a real working example from an asthma study is utilized.